Intelligent Regression Analytics
AutoML Leaderboard
AutoML Performance

AutoML Performance Boxplot

Features Importance (Original Scale)

Scaled Features Importance (MinMax per Model)

Spearman Correlation of Models

Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
0.4 seconds
Metric details
|
score |
threshold |
| logloss |
0.687362 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.717949 |
0.48 |
| accuracy |
0.56 |
0.48 |
| precision |
0.56 |
0.48 |
| recall |
1 |
0.48 |
| mcc |
0 |
0.48 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.687362 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.717949 |
0.48 |
| accuracy |
0.56 |
0.48 |
| precision |
0.56 |
0.48 |
| recall |
1 |
0.48 |
| mcc |
0 |
0.48 |
Confusion matrix (at threshold=0.48)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
11 |
| Labeled as 1 |
0 |
14 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
11.0 seconds
Metric details
|
score |
threshold |
| logloss |
2.38929 |
nan |
| auc |
0.551948 |
nan |
| f1 |
0.777778 |
0 |
| accuracy |
0.68 |
0 |
| precision |
0.636364 |
0 |
| recall |
1 |
0 |
| mcc |
0.416598 |
0 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
2.38929 |
nan |
| auc |
0.551948 |
nan |
| f1 |
0.777778 |
0 |
| accuracy |
0.68 |
0 |
| precision |
0.636364 |
0 |
| recall |
1 |
0 |
| mcc |
0.416598 |
0 |
Confusion matrix (at threshold=0.0)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
3 |
8 |
| Labeled as 1 |
0 |
14 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 3_Linear
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Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
10.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.744102 |
nan |
| auc |
0.233766 |
nan |
| f1 |
0.717949 |
0.401834 |
| accuracy |
0.56 |
0.401834 |
| precision |
0.56 |
0.401834 |
| recall |
1 |
0.401834 |
| mcc |
0 |
0.401834 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.744102 |
nan |
| auc |
0.233766 |
nan |
| f1 |
0.717949 |
0.401834 |
| accuracy |
0.56 |
0.401834 |
| precision |
0.56 |
0.401834 |
| recall |
1 |
0.401834 |
| mcc |
0 |
0.401834 |
Confusion matrix (at threshold=0.401834)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
11 |
| Labeled as 1 |
0 |
14 |
Learning curves

Coefficients
| feature |
Learner_1 |
| feature_1 |
0.22 |
| intercept |
0.219517 |
| feature_2 |
-0.171419 |
Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of 4_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
5.2 seconds
Metric details
|
score |
threshold |
| logloss |
0.688909 |
nan |
| auc |
0.448052 |
nan |
| f1 |
0.717949 |
0.461612 |
| accuracy |
0.56 |
0.461612 |
| precision |
1 |
0.544404 |
| recall |
1 |
0.461612 |
| mcc |
0.327327 |
0.544404 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.688909 |
nan |
| auc |
0.448052 |
nan |
| f1 |
0.717949 |
0.461612 |
| accuracy |
0.56 |
0.461612 |
| precision |
0.56 |
0.461612 |
| recall |
1 |
0.461612 |
| mcc |
0 |
0.461612 |
Confusion matrix (at threshold=0.461612)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
11 |
| Labeled as 1 |
0 |
14 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of 5_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
2.1 seconds
Metric details
|
score |
threshold |
| logloss |
0.740673 |
nan |
| auc |
0.220779 |
nan |
| f1 |
0.717949 |
0.39283 |
| accuracy |
0.56 |
0.39283 |
| precision |
0.56 |
0.39283 |
| recall |
1 |
0.39283 |
| mcc |
0 |
0.39283 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.740673 |
nan |
| auc |
0.220779 |
nan |
| f1 |
0.717949 |
0.39283 |
| accuracy |
0.56 |
0.39283 |
| precision |
0.56 |
0.39283 |
| recall |
1 |
0.39283 |
| mcc |
0 |
0.39283 |
Confusion matrix (at threshold=0.39283)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
11 |
| Labeled as 1 |
0 |
14 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 6_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
3.9 seconds
Metric details
|
score |
threshold |
| logloss |
0.683196 |
nan |
| auc |
0.564935 |
nan |
| f1 |
0.722222 |
0.42674 |
| accuracy |
0.6 |
0.42674 |
| precision |
1 |
0.657309 |
| recall |
1 |
0.343799 |
| mcc |
0.386859 |
0.657309 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.683196 |
nan |
| auc |
0.564935 |
nan |
| f1 |
0.722222 |
0.42674 |
| accuracy |
0.6 |
0.42674 |
| precision |
0.590909 |
0.42674 |
| recall |
0.928571 |
0.42674 |
| mcc |
0.168623 |
0.42674 |
Confusion matrix (at threshold=0.42674)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
2 |
9 |
| Labeled as 1 |
1 |
13 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 1_Baseline |
3 |
| 2_DecisionTree |
1 |
| 6_Default_RandomForest |
2 |
Metric details
|
score |
threshold |
| logloss |
0.678106 |
nan |
| auc |
0.590909 |
nan |
| f1 |
0.777778 |
0.461848 |
| accuracy |
0.68 |
0.461848 |
| precision |
1 |
0.648989 |
| recall |
1 |
0.366272 |
| mcc |
0.416598 |
0.461848 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.678106 |
nan |
| auc |
0.590909 |
nan |
| f1 |
0.777778 |
0.461848 |
| accuracy |
0.68 |
0.461848 |
| precision |
0.636364 |
0.461848 |
| recall |
1 |
0.461848 |
| mcc |
0.416598 |
0.461848 |
Confusion matrix (at threshold=0.461848)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
3 |
8 |
| Labeled as 1 |
0 |
14 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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